Adaptive controllable architecture of analog Ising machine

TL;DR

This paper introduces a controllable analog Ising machine (CAIM) that enhances solution speed and accuracy by integrating control theory and optimization, backed by a theoretical framework and FPGA-based demonstration.

Contribution

It develops a unified mathematical formulation for AIM, proposes CAIM with adaptive control, and demonstrates significant performance improvements over traditional AIMs.

Findings

CAIM achieves a twofold speedup over AIM.

CAIM improves accuracy by 7% on MaxCut problem.

Theoretical framework explains performance bounds of AIMs.

Abstract

As a quantum-inspired, non-traditional analog solver architecture, the analog Ising machine (AIM) has emerged as a distinctive computational paradigm to address the rapidly growing demand for computational power. However, the mathematical understanding of its principles, as well as the optimization of its solution speed and accuracy, remain unclear. In this work, we for the first time systematically discuss multiple implementations of AIM and establish a unified mathematical formulation. On this basis, by treating the binarization constraint of AIM (such as injection locking) as a Lagrange multiplier in optimization theory and combining it with a Lyapunov analysis from dynamical systems theory, an analytical framework for evaluating solution speed and accuracy is constructed, and further demonstrate that conventional AIMs possess a theoretical performance upper bound. Subsequently, by elevating the binarization constraint to a control variable, we propose the controllable analog Ising machine (CAIM), which integrates control Lyapunov functions and momentum-based optimization algorithms to realize adaptive sampling-feedback control, thereby surpassing the performance limits of conventional AIMs. In a proof-of-concept CAIM demonstration implemented using an FPGA-controlled LC-oscillator Ising machine, CAIM achieves a twofold speedup and a 7\% improvement in accuracy over AIM on a 50-node all-to-all weighted MaxCut problem, validating both the effectiveness and interpretability of the proposed theoretical framework.